What if we use Kubernetes? To begin with, this technology allows you to deploy a large number of microservices to different machines, manage them, do autoscaling, etc. However, there are many applications that allow you to control orchestration, for example, Puppet, CF engine, SaltStack, and others. Kubernetes itself is certainly good, but it can add significant overhead, which not every project is ready to live with.

My favorite tool is Ansible, combined with Terraform where you need it. Ansible is a fairly simple declarative lightweight tool. It does not require the installation of special agents and has the understandable syntax of configuration files. If you are familiar with Docker compose, you will immediately see overlapping sections. And if you use Ansible, there is no need to pre-re-reserve – you can deploy systems using more classical means.

It is clear that, these are different technologies, but there are some set of tasks in which they are interchangeable. And a conscientious approach to design requires an analysis of which technology is more suitable for the system being developed. And how it will be better to match it in a few years.

If the number of different services on your system is small and their configuration is relatively simple, for example, you only have one jar file, and you don’t see any sudden, explosive growth in complexity, you can probably get by with classic deployment mechanisms.

This raises the question, “wait, how is one jar file?”. The system should consist of as many atomic microservices as possible! Let’s see how and what the system should do with microservices.

Microservices

First of all, microservices allow achieving greater flexibility and scalability, and allow flexible versioning of individual parts of the system. Suppose we have some kind of application that has been in production for many years. Functionality is growing, but we cannot endlessly develop it in an extensive way. For example.

We have an application in Spring Boot 1 and Java 8. A wonderful, stable combination. But the year is 2019, and whether we like it or not, we need to move towards Spring Boot 2 and Java 12. Even the relatively simple transition of a large system to the new version of Spring Boot can be very laborious, but about jumping from Java 8 to Java 12 I don’t want even to talk. It means, in theory everything is simple: we migrate, correct the problems that have arisen, we test everything and run it in production. In practice, this can mean several months of work that does not bring new functionality to the business. A little move to Java 12, as you know, also does not work. Here microservice architecture can help us.

We can allocate some compact group of functions of our application into a separate service, migrate this group of functions to a new technical stack and roll it into production in a relatively short time. Repeat the process piece by piece until the old technologies are completely exhausted.

Microservices also provide fault isolation when a single dropped component does not destroy the entire system.

Microservices allow us to have a flexible technical stack, and not to write everything monolithically in one language and one version, and if necessary use a different technical stack for individual components. Of course, it is better when you use a uniform technical stack, but this is not always possible, and in this case microservices can help out.

Microservices also allow a technical way to solve a number of managerial problems. For example, when your large team consists of separate groups working in different companies (sitting in different time zones and speaking different languages). Microservices help isolate this organizational diversity by components that will be developed separately. The problems of one part of the team will remain inside one service, and not spread throughout the application.

But microservices are not the only way to solve these problems. A few decades ago, for half of them, people came up with classes, and a little later – components and the Inversion of Control pattern.

If we look at Spring, we see that in fact it is a microservice architecture inside a Java process. We can declare a component, which, in essence, is a service. We have the ability to do a lookup through @Autowired, there are tools for managing the component life cycle and the ability to separately configure components from a dozen different sources. In principle, we get almost everything that we have with microservices – only inside one process, which significantly reduces costs. A regular Java class is the same API contract that also allows you to isolate implementation details.

Strictly speaking, in the Java world, microservices are most similar to OSGi – there we have an almost exact copy of everything that is in microservices, except, besides the possibility of using different programming languages ​​and code execution on different servers. But even staying within the capabilities of Java classes, we have a powerful enough tool to solve a large number of isolation problems.

Even in a “managerial” scenario with team isolation, we can create a separate repository that contains a separate Java module with a clear external contract and a set of tests. This will significantly reduce the ability of one team to complicate the life of another team.

I have repeatedly heard that it is impossible to isolate implementation details without microservices. But I can answer that the entire software industry is just about isolating the implementation. For this, a subroutine was first invented (in the 50s of the last century), then functions, procedures, classes, and later microservices. But the fact that microservices in this series appeared last does not make them the highest point of development and does not oblige us to always resort to their help.

When using microservices, one must also take into account that calls between them take some time. This is often unimportant, but I have seen a case where the customer needed to fit the system response time of 3 seconds. It was a contractual obligation to connect to a third-party system. The chain of calls passed through several dozen atomic microservices, and the overhead of making HTTP calls did not make it possible to go in 3 seconds. In general, you need to understand that any division of monolithic code into a number of services inevitably affects the overall performance of the system. Just because data cannot be teleported between processes and servers “for free.”